Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis
Summary Report for 2022 and 2024
Authors:
Nicole Krampitz1, Julia Mateski1, Dione Swanson2,3 1 CSS, Inc. under contract to NOAA National Ocean Service, National Centers for Coastal Ocean Science 2 NOAA National Marine Fisheries Service, Southeast Fisheries Science Center 3 University of Miami, Cooperative Institute for Marine and Atmospheric Studies
1 Introduction
Mission: Iconic Reefs (M:IR) is a multi-institutional initiative designed to restore ecological function and biodiversity across key reef sites in the Florida Keys. Led by NOAA in collaboration with federal, state, institutional, and non-profit partners, M:IR targets nearly three million square feet of reef at seven key coral reefs in the Florida Keys National Marine Sanctuary (FKNMS). The overarching M:IR goal is to restore coral cover and reef function to a self-sustaining state.
Monitoring the response of coral reef benthic communities and coral populations to reef restoration is essential to quantify changes over time from restoration (Goergen et al. 2022). For this comparison of the status of Iconic Reefs in comparison to Florida Keys reefs, National Coral Reef Monitoring Program (NCRMP) surveys were conducted in 2022 and 2024 by the FKNMS M:IR field team. NCRMP surveys are conducted on shallow (<30 m), hard-bottom reef habitats and employ a stratified-random, one-stage design within 50 m × 50 m grid cells to ensure representative sampling across depth and rugosity strata (Ault et al., 2021). The NCRMP survey design ensures that survey sites are allocated by cross-shelf zone, rugosity type, and depth strata with sites distributed around the Florida Keys from nearshore to offshore to a maximum depth of 30 m. The NCRMP sample grid was spatially joined with the seven distinct MIR zones (Figure 1): Carysfort (North and South), Cheeca Rocks, Eastern Dry Rocks, Horseshoe Reef, Looe Key, Newfound Harbor, and Sombrero Reef. Survey sites were proportionally allocated within NCRMP strata to maintain consistent representation across habitats.
Figure 1: Map of the Florida Keys showing the sites of Mission:Iconic Reefs
Corals and benthic communities were monitored using two different NCRMP surveys: the Benthic Community Assessment survey and the Coral Demographics survey (NOAA CRCP 2022a, 2022b, 2024a, 2024b). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species (NOAA National Marine Fisheries Service, 2014), 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP 2022a, 2024b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP 2022b, 2024b). NCRMP coral demographic survey data were combined with complementarily allocated survey data from Florida Fish and Wildlife Conservation Commission’s Disturbance Response Monitoring (DRM). In all coral demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter, and estimates were made of the proportion per colony of any present mortality (recent or old), disease (absent, present: slow, fast), and/or bleaching (none, total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition (NOAA CRCP 2022b); in 2024, juveniles of select coral species were counted (NOAA CRCP 2024b). In 2024, NCRMP benthic surveys also included large area imaging (LAI) collected at most survey sites: analyses of these LAI data are in progress and not presented here.
To allow direct comparisons of benthic communities inside the restoration areas with the Florida Keys reefs, the NCRMP dataset was restricted to strata types overlapping M:IR sites and depths less than 12 meters. A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments (Blondeau et al., 2025).
In 2022, a total of 584 surveys occurred (Table 1) across 403 sites (Viehman et al., 2023). In 2024, 771 surveys occured across 542 sites (Grove et al., in prep).
Table 1. Still Workshopping. Trying to get it to be in R instead of as a photo but R does NOT like the multi headers
2.1 Benthic Composition
Table 2. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.
Study Area
Strata Name
Strat Description
2022 Sample Number
2024 Sample Number
Inside (M:IR)
CFK01
Inshore reef, all relief types and depths
8
10
Inside (M:IR)
CFK02
Mid-channel patch reef, all relief types and depths
12
12
Inside (M:IR)
CFK03
Offshore patch reef, all relief types and depths
7
8
Inside (M:IR)
CFK04
Forereef, low relief, shallow (0-6 m)
--
3
Inside (M:IR)
CFK05
Forereef, high relief, shallow (0-6 m)
24
33
Inside (M:IR)
CFK06
Forereef, low relief, mid-shallow (6-12 m)
4
9
Inside (M:IR)
CFK07
Forereef, high relief, mid-shallow (6-12 m)
32
28
Outside (NCRMP)
CFK01
Inshore reef, all relief types and depths
10
9
Outside (NCRMP)
CFK02
Mid-channel patch reef, all relief types and depths
21
13
Outside (NCRMP)
CFK03
Offshore patch reef, all relief types and depths
9
8
Outside (NCRMP)
CFK04
Forereef, low relief, shallow (0-6 m)
--
6
Outside (NCRMP)
CFK05
Forereef, high relief, shallow (0-6 m)
7
9
Outside (NCRMP)
CFK06
Forereef, low relief, mid-shallow (6-12 m)
9
7
Outside (NCRMP)
CFK07
Forereef, high relief, mid-shallow (6-12 m)
10
28
2.2 Coral Demography
Table 3. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).
Study Area
Strata Name
Strat Description
2022 Sample Number
2024 Sample Number
Inside (M:IR)
CFK01
Inshore reef, all relief types and depths
12
10
Inside (M:IR)
CFK02
Mid-channel patch reef, all relief types and depths
16
12
Inside (M:IR)
CFK03
Offshore patch reef, all relief types and depths
7
11
Inside (M:IR)
CFK04
Forereef, low relief, shallow (0-6 m)
--
4
Inside (M:IR)
CFK05
Forereef, high relief, shallow (0-6 m)
25
38
Inside (M:IR)
CFK06
Forereef, low relief, mid-shallow (6-12 m)
4
12
Inside (M:IR)
CFK07
Forereef, high relief, mid-shallow (6-12 m)
34
34
Outside (NCRMP + DRM)
CFK01
Inshore reef, all relief types and depths
27
24
Outside (NCRMP + DRM)
CFK02
Mid-channel patch reef, all relief types and depths
79
109
Outside (NCRMP + DRM)
CFK03
Offshore patch reef, all relief types and depths
37
42
Outside (NCRMP + DRM)
CFK04
Forereef, low relief, shallow (0-6 m)
4
31
Outside (NCRMP + DRM)
CFK05
Forereef, high relief, shallow (0-6 m)
21
45
Outside (NCRMP + DRM)
CFK06
Forereef, low relief, mid-shallow (6-12 m)
19
33
Outside (NCRMP + DRM)
CFK07
Forereef, high relief, mid-shallow (6-12 m)
60
61
M:IR selected 10 focal coral species for comparison to NCRMP: Acropora palmata, Acropora cervicornis, Colpophyllia natans, Dendrogyra cylindrus, Dichocoenia stokesii, Diploria labyrinthiformis, Meandrina meandrites, Montastraea cavernosa, Pseudodiploria clivosa, and Pseudodiploria strigosa. The following table summarizes the number of observations across all stratum and survey years of each focal species and identifies the most abundant species encountered.
Table 4. Most Observed Species and Focal M:IR Species Each Year
Species
Colonies in 2022
Colonies in 2024
Siderastrea siderea
3,868
5,618
Porites astreoides
2,657
2,132
Stephanocoenia intersepta
1,345
1,534
Agaricia agaricites
884
237
Porites porites
776
558
*Montastraea cavernosa
437
645
Orbicella faveolata
266
237
Orbicella annularis
211
156
Siderastrea radians
182
328
Porites furcata
160
77
Porites divaricata
118
100
*Dichocoenia stokesii
106
137
*Colpophyllia natans
103
76
Solenastrea bournoni
79
113
*Acropora cervicornis
74
13
*Diploria labyrinthiformis
50
65
*Pseudodiploria strigosa
50
46
Eusmilia fastigiata
45
75
*Pseudodiploria clivosa
23
31
*Acropora palmata
14
NA
*Meandrina meandrites
5
11
* indicates M:IR focal species
3 Benthic Community Composition
Temporal trends in benthic cover of functional groups such as coral and macroalgae cover provide information on changes to the benthic community composition over time.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, Millepora, Seagrass, Bare Substrate, and other miscellaneous organisms.
4 Coral Demography
Insert more text here. Sentence about surveys designed to produce population estimates of coral species. Metrics for status, trends and restoration effects(?) - density, partial mortality, mean colony size, colony size frequency, and colony size partial mortality. Only focal M:IR species are reported below
The following figures for these metrics are grouped by each focal species. Significant statistical differences are indicated for differences between survey years and between areas (MIR vs. NCRMP) for density, old mortality, and mean colony size.
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).
4.1 Coral Density
Figure 4. Caption needed here!
4.2 Old Mortality
Figure 5. Caption needed here!
4.3 Coral Size
Figure 6. Caption needed here too!
4.4 Bleaching Estimates
Figure 7. Individual Species’ Observed Bleaching Frequency Outside (NCRMP + DRM) and Inside (M:IR) areas.
4.5 Disease Estimates
Figure 8. Individual Species’ Observed Diseased Frequency Outside (NCRMP + DRM) and Inside (M:IR) areas.
5 Supplemental Information
Point of Contact: NCRMP Atlantic Benthic Team Lead: Shay Viehman (shay.viehman@noaa.gov)
Data Sources: National Coral Reef Monitoring Program Florida Benthic (NCRMP)
Florida Fish and Wildlife Conservation Commission’s Disturbance Response Monitoring (DRM)
References
Ault, J. S., Smith, S. G., Luo, J., Grove, L. J., Johnson, M. W., & Blondeau, J. (2021). Refinement of the southern florida reef tract benthic habitat map with habitat use patterns of reef fish species [Dataset]. NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/archive/accession/0224176
Blondeau, J. B., Groves, J., & Harper, R. (2025). Mission: Iconic reefs RVC analysis. NOAA Coral Reef Conservation Program.
Groves, S., Mateski, J., Krampitz, N., Sturm, A. M., & Viehman, T. S. (2025). NCRMP benthics statistical package in r (website). R package version 1.0.0. https://github.com/MSE-NCCOS-NOAA/NCRMP_benthics
NOAA Coral Reef Conservation Program (CRCP). (2024a). National coral reef monitoring program (NCRMP) benthic community assessment survey field protocols for u.s. Atlantic: Florida, flower garden banks, puerto rico, and u.s. Virgin islands (p. 29). NOAA Coral Reef Conservation Program. https://doi.org/10.25923/e3gf-vk84
NOAA Coral Reef Conservation Program (CRCP). (2024b). National coral reef monitoring program (NCRMP) coral demographics survey field protocols for u.s. Atlantic: Florida, flower garden banks, puerto rico, u.s. Virgin islands (p. 28). NOAA Coral Reef Conservation Program. https://doi.org/10.25923/hgpr-jm80
NOAA Coral Reef Conservation Program (NOAA CRCP). (2022). National coral reef monitoring program (NCRMP) benthic community assessment survey field protocols for u.s. Atlantic: Florida, flower garden banks, puerto rico, and u.s. Virgin islands–2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/0708-8333
NOAA Coral Reef Conservation Program [NOAA]. (2022). National coral reef monitoring program (NCRMP) coral demographics survey field protocols for u.s. Atlantic: Florida, flower garden banks, puerto rico, u.s. Virgin islands–2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/9a1r-m911
NOAA National Marine Fisheries Service. (2014). Endangered and threatened wildlife and plants: Final listing determinations on proposal to list 66 reef-building coral species and to reclassify elkhorn and staghorn corals. Federal Register, 79(175), 53851–54123.
Viehman, T. S., Grove, J. G., Blondeau, J., Cain, E., Edwards, K. F., Groves, S. H., Krampitz, N., Langwiser, C., Siceloff, L., Swanson, D., Towle, E. K., & Williams, B. (2023). National coral reef monitoring program, biological monitoring summary – florida: 2022 [Technical Memorandum NOS CRCP 48]. NOAA. https://doi.org/10.25923/e6xg-1989
Source Code
---title: "Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis"subtitle: "Summary Report for 2022 and 2024"bibliography: References.bibreference-location: sectionreference-section-title: Referencesnocite: "@NOAA2022a; @NOAA2022b; @NOAA2024a; @NOAA2024b"csl: special.csl format: html: theme: cosmo toc: true toc-depth: 2 number-sections: true code-fold: true code-tools: true fig-cap-location: bottom fig-align: center self-contained: true title-block-banner: true title-block-style: default include-before-body: logos.html pdf: documentclass: article fig-cap-location: bottom includes: in_header: custom_header.texeditor: visualexecute: freeze: auto echo: true warning: false message: false---## Introduction[Mission: Iconic Reefs](https://www.fisheries.noaa.gov/southeast/habitat-conservation/restoring-seven-iconic-reefs-mission-recover-coral-reefs-florida-keys) (M:IR) is a multi-institutional initiative designed to restore ecological function and biodiversity across key reef sites in the Florida Keys. Led by NOAA in collaboration with federal, state, institutional, and non-profit partners, M:IR targets nearly three million square feet of reef at seven key coral reefs in the Florida Keys National Marine Sanctuary (FKNMS). The overarching M:IR goal is to restore coral cover and reef function to a self-sustaining state. Monitoring the response of coral reef benthic communities and coral populations to reef restoration is essential to quantify changes over time from restoration (Goergen et al. 2022). For this comparison of the status of Iconic Reefs in comparison to Florida Keys reefs, National Coral Reef Monitoring Program (NCRMP) surveys were conducted in 2022 and 2024 by the FKNMS M:IR field team. NCRMP surveys are conducted on shallow (\<30 m), hard-bottom reef habitats and employ a stratified-random, one-stage design within 50 m × 50 m grid cells to ensure representative sampling across depth and rugosity strata [@Ault2021]. The NCRMP survey design ensures that survey sites are allocated by cross-shelf zone, rugosity type, and depth strata with sites distributed around the Florida Keys from nearshore to offshore to a maximum depth of 30 m. The NCRMP sample grid was spatially joined with the seven distinct MIR zones (Figure 1): Carysfort (North and South), Cheeca Rocks, Eastern Dry Rocks, Horseshoe Reef, Looe Key, Newfound Harbor, and Sombrero Reef. Survey sites were proportionally allocated within NCRMP strata to maintain consistent representation across habitats. {fig-align="center"}```{r libraries, include = FALSE}# LIBRARIES library(ggplot2)library(dplyr)library(tidyr)library(cowplot)library(grid)library(patchwork)library(ggthemes)library(extrafont)library(grid)library(ggthemes)library(ggpattern)library(extrafont)library(plotly)library(tidyverse)library(ncrmp.benthics.analysis)library(ggplot2)library(cowplot)library(gridExtra)library(egg)library(mgcv)# source("R Scripts/total_dis_code.R")source("../Project/R Scripts/MIR_make_size_bins.R")source("../Project/R Scripts/theme_publication.R")source("../Project/R Scripts/QuadPanel.R")# source("R Scripts/bleaching_estimates.R")# source("R Scripts/total_bleach_code.R")source("../Project/R Scripts/dis_ble_prev_ratio_est.R")``````{r MIR species, echo = FALSE}MIR_species <-c( "ACR CERV", "ACR PALM", "COL NATA", "DEN CYLI","DIC STOK", "DIP LABY", "EUS_ AST", "MEA MEAN","PSE CLIV", "PSE STRI", "MON CAVE" )```Corals and benthic communities were monitored using two different NCRMP surveys: the Benthic Community Assessment survey and the Coral Demographics survey (NOAA CRCP 2022a, 2022b, 2024a, 2024b). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species [@NMFS2014], 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP 2022a, 2024b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP 2022b, 2024b). NCRMP coral demographic survey data were combined with complementarily allocated survey data from Florida Fish and Wildlife Conservation Commission's Disturbance Response Monitoring ([DRM](https://coraldrm.org/)). In all coral demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter, and estimates were made of the proportion per colony of any present mortality (recent or old), disease (absent, present: slow, fast), and/or bleaching (none, total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (\< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition (NOAA CRCP 2022b); in 2024, juveniles of select coral species were counted (NOAA CRCP 2024b). In 2024, NCRMP benthic surveys also included large area imaging (LAI) collected at most survey sites: analyses of these LAI data are in progress and not presented here.To allow direct comparisons of benthic communities inside the restoration areas with the Florida Keys reefs, the NCRMP dataset was restricted to strata types overlapping M:IR sites and depths less than 12 meters. A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments [@Blondeau2025].Sentence about all data using package [@Groves2025]## Summary of Benthic SurveysIn 2022, a total of 584 surveys occurred (Table 1) across 403 sites [@Viehman2023]. In 2024, 771 surveys occured across 542 sites (Grove et al., in prep).```{r, echo=FALSE, fig.cap = "Table 1. Still Workshopping. Trying to get it to be in R instead of as a photo but R does NOT like the multi headers"}knitr::include_graphics("../Project/images/Proper_Table.png")```### Benthic Composition```{r prepare data for benthic cover table, include = FALSE}##Let's just start with MIR/NCRMP only (benthic cover)NCRMP_MIR <-read_csv("../Project/data/NCRMP_MIR_cora_2022_24_psu_grps.csv") %>%group_by(YEAR, strat_cora, analysis_group) %>%summarise(count =n()) %>%filter(analysis_group !="NCRMP_NA") %>%rename(STRAT = strat_cora,PROT = analysis_group) %>%mutate(description =case_when( STRAT =="CFK01"~"Inshore reef, all relief types and depths", STRAT =="CFK02"~"Mid-channel patch reef, all relief types and depths", STRAT =="CFK03"~"Offshore patch reef, all relief types and depths", STRAT =="CFK04"~"Forereef, low relief, shallow (0-6 m)", STRAT =="CFK05"~"Forereef, high relief, shallow (0-6 m)", STRAT =="CFK06"~"Forereef, low relief, mid-shallow (6-12 m)", STRAT =="CFK07"~"Forereef, high relief, mid-shallow (6-12 m)"))table <- NCRMP_MIR %>%pivot_wider(id_cols = STRAT:description, names_from = YEAR, values_from = count) %>%mutate(`Study area`=case_when( PROT =="MIR_GRP"~"Inside (M:IR)", PROT =="NCRMP_GRP"~"Outside (NCRMP)" )) %>%select(`Study area`, STRAT, description, `2022`, `2024`) %>%arrange(`Study area`, STRAT)#conditional statement for rendering in both HTML and PDFif (knitr::is_html_output()) { DT::datatable(table,class ="cell-border stripe",rownames =FALSE,colnames =c("Study Area", "Strata Name", "Strata Description", "2022 Sample Number", "2024 Sample Number"),caption ="Table 1. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.",options =list(columnDefs =list(list(className ='dt-center', targets ="_all")),info =FALSE,paging =FALSE,searching =FALSE ) )}name <-c("<b>Study Area</b>", "<b>Strata Name</b>", "<b>Strat Description</b>", "<b>2022 Sample Number</b>", "<b>2024 Sample Number</b>")invisible(table)``````{r create benthic cover table, echo = FALSE}table <- table %>%ungroup() %>%mutate(across(everything(), ~ifelse(is.na(.), "--", .)))sjPlot::tab_df( table,title ="<div style='text-align:center'>Table 2. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.</div>",escape =FALSE,show.footnote =TRUE,alternate.rows =TRUE,col.header = name,CSS =list(css.caption ="font-weight: normal;") )```### Coral Demography```{r create coral demo table data, include = FALSE}###Demographics (including DRM)DRM <-read_csv("../Project/data/DRM_cora_2022_24_psu_grps.csv") %>%group_by(YEAR, strat_cora, analysis_group) %>%summarise(count =n()) %>%filter(analysis_group !="NCRMP_NA") %>%rename(STRAT = strat_cora,PROT = analysis_group) %>%mutate(description =case_when( STRAT =="CFK01"~"Inshore reef, all relief types and depths", STRAT =="CFK02"~"Mid-channel patch reef, all relief types and depths", STRAT =="CFK03"~"Offshore patch reef, all relief types and depths", STRAT =="CFK04"~"Forereef, low relief, shallow (0-6 m)", STRAT =="CFK05"~"Forereef, high relief, shallow (0-6 m)", STRAT =="CFK06"~"Forereef, low relief, mid-shallow (6-12 m)", STRAT =="CFK07"~"Forereef, high relief, mid-shallow (6-12 m)")) %>%rbind(., NCRMP_MIR) %>%group_by(STRAT, PROT, YEAR, description) %>%summarise(count =sum(count)) %>%ungroup()table_2 <- DRM %>%pivot_wider(id_cols = STRAT:description, names_from = YEAR, values_from = count) %>%mutate(`Study area`=case_when( PROT =="MIR_GRP"~"Inside (M:IR)", PROT =="NCRMP_GRP"~"Outside <br> (NCRMP + DRM)" )) %>%select(`Study area`, STRAT, description, `2022`, `2024`) %>%arrange(`Study area`, STRAT)if (knitr::is_html_output()) { DT::datatable(table_2,class ="cell-border stripe",rownames =FALSE,colnames =c("Study Area", "Strata Name", "Strata Description", "2022 Sample Number", "2024 Sample Number"),caption ="Table 3. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).",options =list(columnDefs =list(list(className ='dt-center', targets ="_all")),info =FALSE,paging =FALSE,searching =FALSE ) )}invisible(table2)``````{r print coral demo table, echo = FALSE}table_2 <- table_2 %>%ungroup() %>%mutate(across(everything(), ~ifelse(is.na(.), "--", .)))sjPlot::tab_df( table_2,title ="<div style='text-align:center'> Table 3. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).</div>",escape =FALSE,show.footnote =TRUE,alternate.rows =TRUE,col.header = name,CSS =list(css.firsttablecol ="width: 140px;", # widen first colcss.th ="font-style: normal; font-weight: bold;", # headers bold, not italiccss.caption ="font-weight: normal;") # title/description not bold)```M:IR selected 10 focal coral species for comparison to NCRMP: *Acropora palmata*, *Acropora cervicornis*, *Colpophyllia natans*, *Dendrogyra cylindrus*, *Dichocoenia stokesii*, *Diploria labyrinthiformis*, *Meandrina meandrites*, *Montastraea cavernosa*, *Pseudodiploria clivosa*, and *Pseudodiploria strigosa*. The following table summarizes the number of observations across all stratum and survey years of each focal species and identifies the most abundant species encountered.```{r make data for top species, include = FALSE}##Everything calculated for density is done so by region/year, so maybe just do total colonies observed?df <-read.csv( "../Project/data/FK2024_NCRMP_DRM_MIR_corsz_AR_NK.csv") %>%filter(N >0, analysis_group !="NCRMP_NA") %>%group_by(SPECIES_CD, YEAR) %>%summarise(observed =n()) %>%arrange(desc(observed)) %>%pivot_wider(id_cols = SPECIES_CD, names_from = YEAR, values_from = observed)##Top 15 speciestop <- df %>%ungroup() %>% dplyr::slice(1:15)##MIR Speciestarget_species <-c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI","DIC STOK", "DIP LABY", "EUS_ AST", "MEA MEAN","PSE CLIV", "PSE STRI", "MON CAVE")MIR <- df %>%filter( SPECIES_CD %in% target_species)##Togethertable <-full_join(MIR, top)##Add names from origial DRM datanames <-read_csv("../Project/data/DRM_FLK_2014_2024_2stage_coral_demographics.csv") %>%select(SPECIES_CD, SPECIES_NAME) %>%unique()final <-left_join(table, names) %>%select(SPECIES_NAME, SPECIES_CD, `2022`, `2024`) %>%arrange(desc(`2022`)) %>%mutate(SPECIES_NAME =ifelse(`SPECIES_CD`%in% target_species,paste0("*", "<i>", SPECIES_NAME, "</i>"),paste0("<i>", SPECIES_NAME, "</i>") )) %>%mutate(across(c(`2022`, `2024`), ~format(., big.mark =",", scientific =FALSE))) %>%ungroup() %>%select(-SPECIES_CD)``````{r create top species table, echo = FALSE}final <- final %>%ungroup() %>%mutate(across(everything(), ~ifelse(is.na(.), "--", .)))sjPlot::tab_df( final,title ="<div style='text-align:center'>Table 4. Most Observed Species and Focal M:IR Species Each Year</div>",alternate.rows =TRUE,show.footnote =TRUE,footnote ="* indicates M:IR focal species",col.header =c("<b>Species</b>","<b>Colonies in 2022</b>","<b>Colonies in 2024</b>" ),CSS =list(css.caption ="font-weight: normal;") )```## Benthic Community CompositionTemporal trends in benthic cover of functional groups such as coral and macroalgae cover provide information on changes to the benthic community composition over time.```{r fig 1: benthic cover data, fig.width=12, fig.asp=0.6, echo = FALSE}#get cover categoriescat_codes <-read_csv("../Project/data/bcov_cat_codes.csv") %>% dplyr::rename(cover_group = CODE)#load and clean cover data (MIR and NCRMP, no DRM)cvr <-read_csv("../Project/data/fk2022_24_NCRMP_MIR_bcov_catcd_dom_NK.csv")%>% dplyr::rename(cover_group = COVER_CAT_CD,REGION = analysis_group,avCvr = wavp,SE = se_wp,n_sites = n ) %>%left_join(cat_codes)``````{r fig_benthic_cover_data (OG), echo = FALSE}#wanted_cover_cats <- c("HCORA" , "MACAL")#Formatting Data for Combined Macroalgal/Hard Coral plotcvr_analysis <- cvr %>%group_by(REGION, COV_CAT_A, YEAR) %>%# filter(COV_CAT_A %in% wanted_cover_cats) %>%summarise(avCvr =sum(avCvr),SE =sqrt(sum(SE^2))#,# LCI_1.96 = avCvr - 1.96 * SE,# UCI_1.96 = avCvr + 1.96 * SE,# LCI = avCvr - SE,# UCI = avCvr + SE )#change names --> more readablenew_dat <- cvr_analysis %>%mutate(COV_CAT_A =recode(COV_CAT_A, "HCORA"="Hard Coral", "MACAL"="Macroalgae","RAMIC"="Ramicrusta","TURFA"="Turf Algae","SPONG"="Sponge","SCORA"="Octocoral","CCALG"="Crustose Coralline Algae (CCA)","OTHER"="Other" ))##Other includes Cyanobacteria, Bare, Palythoa, Other organisms, Millepora, Seagrass## read in significancesig <-read_csv("../Project/data/significance.csv")dodge_width <-0.06# Get unique categoriescategories <-unique(new_dat$COV_CAT_A)# Define the plot for multi-function use in tabsetmake_plot <-function(df, category) { df_filtered <- df %>%filter(COV_CAT_A == category)plot <- df_filtered %>%ggplot(aes(x =as.integer(YEAR), y = avCvr *100, group =interaction(COV_CAT_A, REGION), color = REGION)) +geom_point(size =2, position =position_dodge(width =0.06)) +geom_line(size =1, position =position_dodge(width =0.06)) +geom_errorbar(aes(ymin = avCvr*100- SE*100, ymax = avCvr*100+ SE*100),width =0.1,position =position_dodge(width =0.06)) +scale_color_manual(values =c("MIR_GRP"="#0072B2", "NCRMP_GRP"="#D55E00"),labels =c("M:IR", "NCRMP"),name ="" ) +labs(x ="Year", y ="Cover (%)", title = category ) +# facet_wrap(~ COV_CAT_A) + ##This was when we filtered for just hard corals/macroalgae. If we output as a pdf, might be necessary to bring backscale_x_continuous(breaks =c(2022, 2024)) +#scale_y_continuous(, labels = scales::percent_format(accuracy = 2) ) +theme_Publication(base_size =20) +theme(strip.text =element_text(face ="bold", size =16),legend.position ="bottom",legend.direction ="horizontal",legend.key.size =unit(1.2, "cm"),legend.margin =margin(0, 0, 0, 0, "cm"),legend.text =element_text(size =16),legend.background =element_blank(),legend.title =element_blank() ) +guides(color =guide_legend(override.aes =list(size =4)))##Adding the significance labels#This may take a fair amount of tweaking ##sig_cover <- sig %>%filter(metric =="cover") %>%filter(variable == category)# For between years find a midpoint to go on the plotmidpoints <- df_filtered %>%group_by(REGION) %>%summarise(y_mid =1.02*mean(avCvr *100, na.rm =TRUE))## For between groups find the y min to go on the plotymin <- df_filtered %>%group_by(COV_CAT_A) %>%summarise(y_min =85* (min(avCvr)-max(SE))) %>%#this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = COV_CAT_A) #for joining purposes# Add them to the significance tablesig_cover <- sig_cover %>%left_join(midpoints, by ="REGION") %>%left_join(ymin, by ="variable")##Plot it with annotations(plot +### Significance BETWEEN GROUPS on X axisgeom_text(data = sig_cover %>%filter (is.na(REGION)),aes(x = YEAR, y = y_min), label ="+",inherit.aes =FALSE, size =12, color ="black") +### Significance BETWEEN YEARS on graphgeom_text(data = sig_cover %>%filter (is.na(YEAR)),aes(x =2023, y = y_mid, color = REGION), label ="*",inherit.aes =FALSE, size =15))}``````{r loop through cover plots and tab results, fig.cap="Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, <i>Millepora</i>, Seagrass, Bare Substrate, and other miscellaneous organisms.", fig.width=9, results='asis', fig.asp=0.8, echo=FALSE}# Loop through categories and print plotscat("::: {.panel-tabset}\n\n") # open tabset container# Define manual tab orderordered_categories <-c("Hard Coral", "Macroalgae", "Octocoral", "Sponge", "Turf Algae", "Crustose Coralline Algae (CCA)", "Ramicrusta", "Other")walk(ordered_categories, function(cat) {cat("### ", cat, " {.tab-pane}\n\n", sep ="") cat_plot <-make_plot(new_dat, cat)print(cat_plot)cat("\n\n")})cat(":::\n") # close tabset container```## Coral DemographyInsert more text here. Sentence about surveys designed to produce population estimates of coral species. Metrics for status, trends and restoration effects(?) - density, partial mortality, mean colony size, colony size frequency, and colony size partial mortality. Only focal M:IR species are reported belowThe following figures for these metrics are grouped by each focal species. Significant statistical differences are indicated for differences between survey years and between areas (MIR vs. NCRMP) for density, old mortality, and mean colony size.```{r prep density data, fig.width=12, fig.asp=0.6, echo = FALSE}# Read in clean dens dataden <-read_csv("../Project/data/fk2022_24_NCRMP_MIR_corabd_dom_NK.csv") %>%rename(avDen = wavdns,SE = se_wdns,CV_perc = wcv_dns,n_sites = n)#load full species namenames <-read_csv("../Project/data/NCRMP_FKEYS2022_Benthic_Data02_CoralDemographics.csv") %>%select(SPECIES_CD, SPECIES_NAME)#Join w species namesden <- den %>%left_join(names, by ="SPECIES_CD")``````{r find top species, echo = FALSE}# Load data to find top speciesAR_ready_demo <-read_csv("../Project/data/FK2024_NCRMP_DRM_MIR_corsz_AR_NK.csv")df <- AR_ready_demo %>%filter(N ==1) %>%group_by(SPECIES_CD) %>%summarise(total =n(), .groups ="drop") %>%arrange(desc(total)) %>%#filter(SPECIES_CD %in% codes) %>%slice(1:10)topspecies <- df$SPECIES_CD# Combine top species with MIR_speciestarget_species <-unique(c(topspecies, MIR_species))``````{r prep data, echo = FALSE}tmp <-quad_panel_MIR(target_species)# Prepare Density Dataa <- tmp$AR_density %>%select(YEAR, SPECIES_CD, wavdns, se_wdns, analysis_group) %>%rename(avDen = wavdns, SE_avDen = se_wdns)# Prepare old mort datab <- tmp$AR_mortality %>%filter(MORT_TYPE =="Old") %>%select(YEAR, SPECIES_CD, avMort, SE, analysis_group) %>%rename(avMortOld = avMort, SE_avMortOld = SE)# old_mortality_ready_for_stat_testing <- b %>%# mutate(UCI = avMortOld + SE_avMortOld,# LCI = avMortOld - SE_avMortOld)# # view(old_mortality_ready_for_stat_testing)# # readr::write_csv(old_mortality_ready_for_stat_testing, "old_mortality_ready_for_stat_testing.csv")# # dens_ready_for_stat_testing <- a%>%# mutate(UCI = avDen + SE_avDen,# LCI = avDen - SE_avDen)# # # readr::write_csv(dens_ready_for_stat_testing, "dens_ready_for_stat_testing.csv")# # # view(dens_ready_for_stat_testing)# Prepare size data# c <- tmp$AR_col_size %>%# mutate(SE_maxdiam = sqrt(var_maxdiam)) %>%# rename(avMaxdiam = avg_maxdiam) %>%# select(REGION, YEAR, SPECIES_CD, avMaxdiam, SE_maxdiam, analysis_group)#RATIO ESTIMATIONratio_est_2022 <-read_csv("../Project/data/fk2022_NCRMP_MIR_sppLbar_dom.csv")ratio_est_2024 <-read_csv("../Project/data/fk2024_NCRMP_MIR_sppLbar_domv2.csv")c <-bind_rows(ratio_est_2022, ratio_est_2024) %>%rename(avMaxdiam = Lbar,SE_maxdiam = SE_L)# Prep make size binstmp <-MIR_make_size_bins(years =c(2022, 2024),analyzed_species = MIR_species)#gather and clean domain Estdomain_estimates <- tmp$length_freq_domain_est %>% dplyr::mutate(YEAR =as.factor(YEAR)) %>% dplyr::ungroup()#gather and clean length demosdemos <- tmp$length_demos %>% dplyr::mutate(YEAR =as.factor(YEAR)) %>% dplyr::ungroup()#get domain mortality datadomain_mort <- tmp$domain_mort_spp# write_csv(a, "../Project/denisty_data.csv")# write_csv(b, "../Project/old_mort.csv")# write_csv(c, "../Project/max_diam.csv")``````{r create_species_plots function, fig.width=9, fig.asp=1.2, echo = FALSE}create_species_plots <-function(species) { den_filtered <- a %>%filter(SPECIES_CD == species) b_filtered <- b %>%filter(SPECIES_CD == species) c_filtered <- c %>%filter(SPECIES_CD == species)if (nrow(den_filtered) ==0||nrow(b_filtered) ==0||nrow(c_filtered) ==0) {return(NULL) }dodge_width <-0.05### DENSITY PLOT ########## plt1 <- den_filtered %>%ggplot(aes(x = YEAR, y = avDen, group = analysis_group, color = analysis_group)) +geom_point(position =position_dodge(dodge_width)) +geom_line(position =position_dodge(dodge_width)) +geom_errorbar(aes(ymin = avDen - SE_avDen, ymax = avDen + SE_avDen), width =0.1, position =position_dodge(dodge_width)) +labs(y ="Density (col/m2)", x ="Sample Year", title ="Coral Density") +theme_Publication(base_size =10) +theme(strip.text =element_text(face ="italic"))+theme(legend.position ="none") +scale_x_continuous(breaks =c(2022, 2024)) +scale_color_manual(values =c("#0072B2", "#D55E00"), labels =c("MIR", "Outside"), name ="") +scale_linetype_manual(values =c("solid", "dashed"), guide ="none")##Adding the significance labels#This may take a fair amount of tweaking ##sig_cover <- sig %>%filter(metric =="density") %>%filter(variable == species)# For between years find a midpoint to go on the plotmidpoints <- den_filtered %>%group_by(analysis_group) %>%summarise(y_mid =1.02*mean(avDen, na.rm =TRUE)) %>% dplyr::rename(REGION = analysis_group) #for joining purposes## For between groups find the y min to go on the plotymin <- den_filtered %>%group_by(SPECIES_CD) %>%summarise(y_min = .85* (min(avDen)-max(SE_avDen))) %>%#this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = SPECIES_CD) #for joining purposes# Add them to the significance tablesig_cover <- sig_cover %>%left_join(midpoints, by ="REGION") %>%left_join(ymin, by ="variable")##Plot it with annotationsplt1 <- plt1 +### Significance BETWEEN GROUPS on X axisgeom_text(data = sig_cover %>%filter (is.na(REGION)),aes(x = YEAR, y = y_min), label ="+",inherit.aes =FALSE, size =8, color ="black") +### Significance BETWEEN YEARS on graphgeom_text(data = sig_cover %>%filter (is.na(YEAR)),aes(x =2023, y = y_mid, color = REGION), label ="*",inherit.aes =FALSE, size =10)### MORTALITY PLOT ########## plt2 <- b_filtered %>%ggplot(aes(x = YEAR, y = avMortOld, group = analysis_group, color = analysis_group)) +geom_point(position =position_dodge(dodge_width)) +geom_line(position =position_dodge(dodge_width)) +geom_errorbar(aes(ymin = avMortOld - SE_avMortOld, ymax = avMortOld + SE_avMortOld), width =0.1, position =position_dodge(dodge_width)) +labs(y ="Old Mortality (%)", x ="Sample Year", title ="Old Mortality") +theme_Publication(base_size =10) +theme(strip.text =element_text(face ="italic"))+theme(legend.position ="none") +scale_x_continuous(breaks =c(2022, 2024)) +scale_color_manual(values =c("#0072B2", "#D55E00"), labels =c("MIR", "Outside"), name ="") +scale_linetype_manual(values =c("solid", "dashed"), guide ="none")##Adding the significance labels#This may take a fair amount of tweaking ##sig_cover <- sig %>%filter(metric =="mortality") %>%filter(variable == species)# For between years find a midpoint to go on the plotmidpoints <- b_filtered %>%group_by(analysis_group) %>%summarise(y_mid =1.02*mean(avMortOld, na.rm =TRUE)) %>% dplyr::rename(REGION = analysis_group) #for joining purposes## For between groups find the y min to go on the plotymin <- b_filtered %>%mutate(YEARS =mean(YEAR)) %>%group_by(SPECIES_CD, YEARS) %>%summarise(y_min = .85* (min(avMortOld)-max(SE_avMortOld))) %>%#changed here because APAL only has 2022 dplyr::rename(variable = SPECIES_CD) #for joining purposes# Add them to the significance tablesig_cover <- sig_cover %>%left_join(midpoints, by ="REGION") %>%left_join(ymin, by ="variable")##Plot it with annotationsplt2 <- plt2 +### Significance BETWEEN GROUPS on X axisgeom_text(data = sig_cover %>%filter (is.na(REGION)),aes(x = YEAR, y = y_min), label ="+",inherit.aes =FALSE, size =8, color ="black") +### Significance BETWEEN YEARS on graphgeom_text(data = sig_cover %>%filter (is.na(YEAR)),aes(x = YEARS, y = y_mid, color = REGION), label ="*",inherit.aes =FALSE, size =10) plt3 <- c_filtered %>%ggplot(aes(x = YEAR, y = avMaxdiam, group = analysis_group, color = analysis_group)) +geom_point(position =position_dodge(dodge_width)) +geom_line(position =position_dodge(dodge_width)) +geom_errorbar(aes(ymin = avMaxdiam - SE_maxdiam, ymax = avMaxdiam + SE_maxdiam), width =0.1, position =position_dodge(dodge_width)) +labs(y ="Mean Max Diameter (cm)", x ="Sample Year", title ="Colony Size") +theme_Publication(base_size =10) +theme(strip.text =element_text(face ="italic"))+theme(legend.position ="none") +scale_x_continuous(breaks =c(2022, 2024)) +scale_color_manual(values =c("#0072B2", "#D55E00"), labels =c("MIR", "Outside"), name ="") +scale_linetype_manual(values =c("solid", "dashed"), guide ="none")##Adding the significance labels#This may take a fair amount of tweaking ##sig_cover <- sig %>%filter(metric =="mortality") %>%filter(variable == species)# For between years find a midpoint to go on the plotmidpoints <- c_filtered %>%group_by(analysis_group) %>%summarise(y_mid =1.02*mean(avMaxdiam), na.rm =TRUE) %>% dplyr::rename(REGION = analysis_group) #for joining purposes## For between groups find the y min to go on the plotymin <- c_filtered %>%group_by(SPECIES_CD) %>%summarise(y_min = .85* (min(avMaxdiam)-max(SE_maxdiam))) %>%#this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = SPECIES_CD) #for joining purposes# Add them to the significance tablesig_cover <- sig_cover %>%left_join(midpoints, by ="REGION") %>%left_join(ymin, by ="variable")##Plot it with annotationsplt3 <- plt3 +### Significance BETWEEN GROUPS on X axisgeom_text(data = sig_cover %>%filter (is.na(REGION)),aes(x = YEAR, y = y_min), label ="+",inherit.aes =FALSE, size =8, color ="black") +### Significance BETWEEN YEARS on graphgeom_text(data = sig_cover %>%filter (is.na(YEAR)),aes(x =2023, y = y_mid, color = REGION), label ="*",inherit.aes =FALSE, size =10) domain_sub =subset(domain_estimates, SPECIES_CD == species) demos_sub =subset(demos, SPECIES_CD == species) mort_sub = domain_mort %>% dplyr::filter(SPECIES_CD == species) domain_sub <- domain_sub %>%# add 0s in for species not observed tidyr::expand(., REGION, analysis_group, SPECIES_CD, YEAR, bin_num) %>%#filter(YEAR %in% years) %>% #Filter back to years analyzed only# connect back to demo_data to fill in with NAs dplyr::full_join(., domain_sub) %>%# make a presence/absence column dplyr::mutate(length_freq_domain =ifelse(is.na(length_freq_domain), 0, length_freq_domain)) %>%# add in mortality data dplyr::full_join(., mort_sub) n_bins =max(demos_sub$n_bins) text_size =14 angle =45 hjust = dplyr::if_else(angle ==45, 1, 0.5) min =min(demos_sub$min) bin_width =min(demos_sub$bin_width) small = dplyr::if_else(n_bins >4, 0, 1) num_vec =seq_len(max(domain_sub$bin_num)) lab_vec <-c("4- 10", " 11-15", " 16-20", " 21-25", " 26-30", " 31-35", " 36-45", " 46-65", " 66-85", " 86-105", "106+") species_name <- names %>%filter(SPECIES_CD == species) %>%distinct(SPECIES_NAME) %>%pull(SPECIES_NAME) plt4 <-ggplot(data = domain_sub, aes(x =as.integer(bin_num), y = length_freq_domain, fill = analysis_group)) +geom_bar(stat ="identity", position ="dodge2", width =0.9, color ="black", size =0.5) +geom_point(aes(y = oldmort_domain, color = analysis_group), size =2) +geom_line(aes(y = oldmort_domain, color = analysis_group), size =2, alpha =0.5) +facet_wrap(~YEAR) +labs(y ="Frequency (Size Class & Mortality)", x ="Length (cm)") +scale_x_continuous(breaks =seq_along(lab_vec), labels = lab_vec) +scale_fill_manual(values =c("#0072B2", "#D55E00"), labels =c("M:IR", "NCRMP + DRM")) +scale_color_manual(values =c("#0072B2", "#D55E00"), guide ="none") +theme_Publication(base_size =10) +theme(axis.text.x =element_text(size = text_size, angle = angle, hjust = hjust),legend.title =element_blank(),strip.text =element_text(face ="italic"))#what goes between the three plots and the size freq/mort plot middle_annotation <-ggplot() +geom_text(aes(x =0.5, y =0.5, label ="Size Frequency Distributions"), size =5, fontface ="bold") +theme_void() +theme(plot.margin =margin(0, 0, 0, 0))library(magick)library(png)library(jpeg)species_images <-list("Acropora cervicornis"=list(path ="../Project/coral_image_files/staghorn.jpg", type ="jpg"),"Colpophyllia natans"=list(path ="../Project/coral_image_files/col_nata.jpg", type ="jpg"),"Dichocoenia stokesii"=list(path ="../Project/coral_image_files/dic_stok.jpg", type ="jpg"),"Diploria labyrinthiformis"=list(path ="../Project/coral_image_files/dip_laby.jpg", type ="jpg"),"Meandrina meandrites"=list(path ="../Project/coral_image_files/mea_mean.jpg", type ="jpg"),"Pseudodiploria clivosa"=list(path ="../Project/coral_image_files/pse_cliv.png", type ="png"),"Pseudodiploria strigosa"=list(path ="../Project/coral_image_files/pse_stri.png", type ="png"),"Montastraea cavernosa"=list(path ="../Project/coral_image_files/mon_cave.jpg", type ="jpg"),"Acropora palmata"=list(path ="../Project/coral_image_files/acr_palm.png", type ="png"))get_species_image_plot <-function(species_name) { info <- species_images[[species_name]]if (!is.null(info)) { img <-switch(info$type,png = png::readPNG(info$path),jpg = jpeg::readJPEG(info$path)) cowplot::ggdraw() + cowplot::draw_image(img) } else {ggplot() +theme_void() }}image_plot <-get_species_image_plot(species_name)final_plot <- (image_plot/ (plt1 + plt2 + plt3) / middle_annotation / plt4) +plot_layout(heights =c(1, 1, 0.1, 1.4)) +plot_annotation(title =paste0("<b><i>", species_name, "</i></b>"),subtitle ="Colony density, mortality, size, and size class distribution (2022 vs. 2024)",theme =theme(plot.title = ggtext::element_markdown(hjust =0.5, size =18),plot.subtitle =element_text(hjust =0.5, size =14) ))return(final_plot)}plots_list <-map(MIR_species, create_species_plots)names(plots_list) <- MIR_specieslst_clean <-compact(plots_list)```<!-- Tabs for HTML output -->```{r coral-tabyss, echo=FALSE, fig.cap="Figure 3. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). A black (+) indicates a significant difference in cover estimates between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance between years for the given group (i.e., M:IR or NCRMP).", results='asis', fig.width=9, fig.asp=1.2}cat("::: {.panel-tabset}\n\n") # open tabset containerwalk(seq_along(lst_clean), function(i) { plot <- lst_clean[[i]] species_code <-names(lst_clean)[[i]] species_name <- names %>%filter(SPECIES_CD == species_code) %>%distinct(SPECIES_NAME) %>%pull(SPECIES_NAME)cat("### <em>", species_name, "</em> {.tab-pane}\n\n")print(plot)})cat("\n:::\n") # close tabset container```### Coral Density```{r overall Density, fig.cap = "Figure 4. Caption needed here!", fig.width=9, fig.asp=1.2, echo = FALSE}###Adding species names and starring MIR speciesa <-left_join(a, names %>%distinct(), by ="SPECIES_CD") %>% dplyr::mutate(SPECIES_NAME =case_when( SPECIES_CD %in% MIR_species ~paste0("*", SPECIES_NAME),TRUE~as.character(SPECIES_NAME))) #Factor re-order for aesthetics a <- a %>%mutate(SPECIES_NAME =fct_reorder(SPECIES_NAME, if_else(is.na(avDen), 0, avDen), .desc =FALSE)) plt1 <- a %>%ggplot(aes(x = avDen, y = SPECIES_NAME, group =interaction(SPECIES_CD, analysis_group), color = analysis_group)) +geom_point( position =position_dodge(width =0.3)) +geom_errorbar(aes(xmin = avDen - SE_avDen, xmax = avDen + SE_avDen), width =0.1, position =position_dodge(width =0.3)) +labs(y ="Species", x =expression("Density (col/m"^2*")")) +theme_Publication(base_size =10) +facet_grid(~YEAR) +scale_x_continuous(expand =expansion(mult =c(0, 0))) +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')")))+theme(strip.text =element_text(face ="italic"))+scale_color_manual(values =c("#0072B2", "#D55E00"), labels =c("MIR", "NCRMP + DRM"), name ="") +scale_linetype_manual(values =c("solid", "dashed"), guide ="none") +theme(strip.text =element_text(size =14, face ="bold"),axis.text.y =element_text(size =11),axis.title =element_text(size =13),legend.position ="bottom",legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) print(plt1)```### Old Mortality```{r overall Old_Mort, fig.cap = "Figure 5. Caption needed here!", fig.width=9, fig.asp=1.2, echo = FALSE}###Adding species names and starring MIR speciesb <-left_join(b, names %>%distinct(), by ="SPECIES_CD") %>% dplyr::mutate(SPECIES_NAME =case_when( SPECIES_CD %in% MIR_species ~paste0("*", SPECIES_NAME),TRUE~as.character(SPECIES_NAME))) #Factor re-order for aesthetics b <- b %>%mutate(SPECIES_NAME =fct_reorder(SPECIES_NAME, if_else(is.na(avMortOld), 0, avMortOld), .desc =FALSE)) plt1 <- b %>%ggplot(aes(x = avMortOld, y = SPECIES_NAME, group =interaction(SPECIES_CD, analysis_group), color = analysis_group)) +geom_point( position =position_dodge(width =0.3)) +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')")))+geom_errorbar(aes(xmin = avMortOld - SE_avMortOld, xmax = avMortOld + SE_avMortOld), width =0.1, position =position_dodge(width =0.3)) +labs(y ="Species", x ="Old Mortality (%)") +theme_Publication(base_size =10) +facet_grid(~YEAR) +scale_x_continuous(expand =expansion(mult =c(0, 0))) +theme(strip.text =element_text(face ="italic"))+scale_color_manual(values =c("#0072B2", "#D55E00"), labels =c("MIR", "NCRMP + DRM"), name ="") +scale_linetype_manual(values =c("solid", "dashed"), guide ="none") +theme(strip.text =element_text(size =14, face ="bold"),axis.text.y =element_text(size =11),axis.title =element_text(size =13),legend.position ="bottom",legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) print(plt1)```### Coral Size```{r overall Max_Dim, fig.cap = "Figure 6. Caption needed here too!", fig.width=9, fig.asp=1.2, echo = FALSE}spec_unq <-unique(b$SPECIES_CD)###Adding species names and starring MIR speciesc <-left_join(c, names %>%distinct(), by ="SPECIES_CD") %>% dplyr::mutate(SPECIES_NAME =case_when( SPECIES_CD %in% MIR_species ~paste0("*", SPECIES_NAME),TRUE~as.character(SPECIES_NAME))) #Factor re-order for aesthetics c <- c %>%mutate(SPECIES_NAME =fct_reorder(SPECIES_NAME, if_else(is.na(avMaxdiam), 0, avMaxdiam), .desc =FALSE))c <- c %>%filter(SPECIES_CD %in% spec_unq) plt1 <- c %>%ggplot(aes(x = avMaxdiam, y = SPECIES_NAME, group =interaction(SPECIES_CD, analysis_group), color = analysis_group)) +geom_point( position =position_dodge(width =0.3)) +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')")))+geom_errorbar(aes(xmin = avMaxdiam - SE_maxdiam, xmax = avMaxdiam + SE_maxdiam), width =0.1, position =position_dodge(width =0.3)) +labs(y ="Species", x ="Average Mean Colony Size") +theme_Publication(base_size =10) +facet_grid(~YEAR) +scale_x_continuous(expand =expansion(mult =c(0, 0))) +theme(strip.text =element_text(face ="italic"))+scale_color_manual(values =c("#0072B2", "#D55E00"), labels =c("MIR", "Outside"), name ="") +scale_linetype_manual(values =c("solid", "dashed"), guide ="none") +theme(strip.text =element_text(size =14, face ="bold"),axis.text.y =element_text(size =11),axis.title =element_text(size =13),legend.position ="bottom",legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) print(plt1)```### Bleaching Estimates```{r bleach 2022, echo = FALSE}# Set path to data filesdata_2022_path <-"data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"MIR_species <-c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")#Script works for the actual value but CI are not exact# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%# filter(PV_TYPE == "Bleach_All") %>%# left_join(., names)names_clean <- names %>%select(SPECIES_CD, SPECIES_NAME) %>%distinct(SPECIES_CD, .keep_all =TRUE)tmp_ble_2022 <-read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>%left_join(., names_clean, by ="SPECIES_CD")tmp <-read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>%left_join(names_clean, by ="SPECIES_CD")tmp_ble <-full_join(tmp_ble_2022, tmp)top_species <- tmp_ble %>%group_by(SPECIES_CD) %>%summarise(mean_pv =mean(PVbar, na.rm =TRUE)) %>%arrange(desc(mean_pv)) %>%slice_head(n =10) %>%pull(SPECIES_CD)tmp_filtered <- tmp_ble %>%filter(SPECIES_CD %in%union(top_species, MIR_species)) ##Add a * for those MIR speciestmp_filtered <- tmp_filtered %>%mutate(SPECIES_NAME =case_when( SPECIES_CD %in% MIR_species ~paste0("*", SPECIES_NAME),TRUE~as.character(SPECIES_NAME)))#Factor re-order for aesthetics tmp_filtered <- tmp_filtered %>%mutate(SPECIES_NAME =fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc =FALSE),YEAR =factor(YEAR, levels =c("2022", "2024")),analysis_group =recode(analysis_group,"MIR_GRP"="MIR","NCRMP_GRP"="NCRMP + DRM"))plt_bleach_MIR <- tmp_filtered %>%filter(SPECIES_NAME !="NA") %>%filter(analysis_group =="MIR") %>%ggplot(aes(x = PVbar, y = SPECIES_NAME, pattern = YEAR, group =rev(YEAR))) + ggpattern::geom_col_pattern(position =position_dodge(width =0.8),width =0.8,fill ="#0072B2", # background color (used if no pattern)pattern_fill ="white", # color of the pattern lines (overlay)pattern_spacing =0.0075, # very tight patternpattern_size =0.025, # thin linespattern_density = .4# fully patterned ) +scale_pattern_manual(values =c("2022"="none", "2024"="stripe")) +geom_errorbar(aes(xmin =pmax(PV_LCI, 0), xmax = PV_UCI), position =position_dodge(width =0.8), width =0.1) +labs(x ="Proportion of Colonies Bleached", y ="Coral Species", fill ="") +scale_x_continuous(expand =expansion(mult =c(0, 0.1))) +theme_Publication(base_size =10) +coord_cartesian(xlim =c(0, 10), clip ="off") +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')"))) +theme(strip.text =element_text(size =14, face ="bold"),axis.text.y =element_text(size =11),axis.title =element_text(size =13),legend.position ="bottom",title =element_text(size =14),legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) +ggtitle("Mission: Iconic Reef")plt_bleach_NCRMP <- tmp_filtered %>%filter(SPECIES_NAME !="NA") %>%filter(analysis_group =="NCRMP + DRM") %>%ggplot(aes(x = PVbar, y = SPECIES_NAME, pattern = YEAR, group =rev(YEAR))) + ggpattern::geom_col_pattern(position =position_dodge(width =0.8),width =0.8,fill ="#D55E00", # background color (used if no pattern)pattern_fill ="white", # color of the pattern lines (overlay)pattern_spacing =0.0075, #75 # very tight patternpattern_size =0.025, # thin linespattern_density = .4# fully patterned ) +scale_pattern_manual(values =c("2022"="none", "2024"="stripe")) +geom_errorbar(aes(xmin =pmax(PV_LCI, 0), xmax = PV_UCI), position =position_dodge(width =0.8), width =0.1) +# facet_wrap(~analysis_group) +labs(x ="Proportion of Colonies Bleached", y ="Coral Species", fill ="") +scale_x_continuous(expand =expansion(mult =c(0, 0.1))) +theme_Publication(base_size =10) +coord_cartesian(xlim =c(0, 10), clip ="off") +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')"))) +theme(strip.text =element_text(size =14, face ="bold"),axis.title.y =element_blank(),axis.text.y =element_blank(),axis.ticks.y =element_blank(),axis.title =element_text(size =13),title =element_text(size =14),legend.position ="bottom",legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) +ggtitle("NCRMP + DRM")``````{r bleaching, fig.cap = "Figure 7. Individual Species’ Observed Bleaching Frequency Outside (NCRMP + DRM) and Inside (M:IR) areas.", fig.width=10, fig.asp=1.4, echo = FALSE}# Combine the two plots side-by-sidefinal_plot <- (plt_bleach_MIR + plt_bleach_NCRMP) +plot_layout(ncol =2) # Ensures side-by-side layoutprint(final_plot)```### Disease Estimates```{r disease, echo= FALSE}# Set path to data filesdata_2022_path <-"../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"MIR_species <-c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")names_clean <- names %>%select(SPECIES_CD, SPECIES_NAME) %>%distinct(SPECIES_CD, .keep_all =TRUE)tmp_dis_2022 <-read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>%left_join(names_clean, by ="SPECIES_CD")tmp_dis_2024 <-read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>%left_join(names_clean, by ="SPECIES_CD")tmp_dis <-full_join(tmp_dis_2022, tmp_dis_2024)top_species <- tmp_dis %>%group_by(SPECIES_CD) %>%summarise(mean_pv =mean(PVbar, na.rm =TRUE)) %>%arrange(desc(mean_pv)) %>%slice_head(n =10) %>%pull(SPECIES_CD)tmp_filtered <- tmp_dis %>%filter(SPECIES_CD %in%union(top_species, MIR_species)) ##Add a * for those MIR speciestmp_filtered <- tmp_filtered %>%mutate(SPECIES_NAME =case_when( SPECIES_CD %in% MIR_species ~paste0( "*", SPECIES_NAME),TRUE~as.character(SPECIES_NAME)))#Factor re-order for aesthetics tmp_filtered <- tmp_filtered %>%mutate(SPECIES_NAME =fct_reorder(SPECIES_NAME, PVbar, .fun =function(x) max(x, na.rm =TRUE), .desc =FALSE),YEAR =factor(YEAR, levels =c("2022", "2024")),analysis_group =recode(analysis_group,"MIR_GRP"="MIR","NCRMP_GRP"="NCRMP + DRM"))plt_dis_MIR <- tmp_filtered %>%filter(SPECIES_NAME !="NA") %>%filter(analysis_group =="MIR") %>%ggplot(aes(x = PVbar, y = SPECIES_NAME, group =rev(YEAR), pattern = YEAR)) + ggpattern::geom_col_pattern(position =position_dodge(width =0.8),width =0.8,fill ="#0072B2", pattern_fill ="white", pattern_spacing =0.0075, pattern_size =0.025, pattern_density = .4 ) +scale_pattern_manual(values =c("2022"="none", "2024"="stripe")) +geom_errorbar(aes(xmin =pmax(PV_LCI, 0), xmax = PV_UCI),position =position_dodge(width =0.8), width =0.1) +labs(x ="Proportion of Colonies Disease", y ="Coral Species", fill ="") +scale_x_continuous(expand =expansion(mult =c(0, 0.1))) +theme_Publication(base_size =10) +coord_cartesian(xlim =c(0, 2), clip ="off") +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')"))) +theme(strip.text =element_text(size =14, face ="bold"),axis.text.y =element_text(size =11),axis.title =element_text(size =13),legend.position ="bottom",title =element_text(size =14),legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) +ggtitle("Mission: Iconic Reef")plt_dis_NCRMP <- tmp_filtered %>%filter(SPECIES_NAME !="NA") %>%filter(analysis_group =="NCRMP + DRM") %>%ggplot(aes(x = PVbar, y = SPECIES_NAME, group =rev(YEAR), pattern = YEAR)) + ggpattern::geom_col_pattern(position =position_dodge(width =0.8),width =0.8,fill ="#D55E00", pattern_fill ="white", pattern_spacing =0.0075, pattern_size =0.025, pattern_density = .4 ) +scale_pattern_manual(values =c("2022"="none", "2024"="stripe")) +geom_errorbar(aes(xmin =pmax(PV_LCI, 0), xmax = PV_UCI),position =position_dodge(width =0.8), width =0.1) +labs(x ="Proportion of Colonies Disease", y ="Coral Species", fill ="") +scale_x_continuous(expand =expansion(mult =c(0, 0.1))) +theme_Publication(base_size =10) +coord_cartesian(xlim =c(0, 2), clip ="off") +scale_y_discrete(labels =function(x) parse(text =paste0("italic('", x, "')"))) +theme(strip.text =element_text(size =14, face ="bold"),axis.title.y =element_blank(),axis.text.y =element_blank(),axis.ticks.y =element_blank(),axis.title =element_text(size =13),title =element_text(size =14),legend.position ="bottom",legend.text =element_text(size =10),panel.spacing =unit(1, "lines") ) +ggtitle("NCRMP + DRM")``````{r, echo = FALSE, fig.cap = "Figure 8. Individual Species’ Observed Diseased Frequency Outside (NCRMP + DRM) and Inside (M:IR) areas.", fig.width=10, fig.asp=1.4}# Combine the two plots side-by-side(final_plot <- (plt_dis_MIR + plt_dis_NCRMP) +plot_layout(ncol =2))```## Supplemental Information**Point of Contact**:\*NCRMP Atlantic Benthic Team Lead:* Shay Viehman ([shay.viehman\@noaa.gov](mailto:shay.viehman@noaa.gov))**Data Sources:\**National Coral Reef Monitoring Program Florida Benthic ([NCRMP](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:NCRMP-Benthic-Florida))\Florida Fish and Wildlife Conservation Commission's Disturbance Response Monitoring ([DRM](https://coraldrm.org/))
